Locating Optimization of an Integrated Energy Supply Centre in a Typical New District Based on the Load Density
In view of the obvious distinctions among energy utilization characteristics in various functional districts, a method for optimizing the location of an integrated energy system for a typical new district is proposed. This method integrates the spatial location and energy load density of each functi...
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| Vydáno v: | Energies (Basel) Ročník 11; číslo 4; s. 934 |
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| Jazyk: | angličtina |
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Basel
MDPI AG
01.04.2018
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| ISSN: | 1996-1073, 1996-1073 |
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| Abstract | In view of the obvious distinctions among energy utilization characteristics in various functional districts, a method for optimizing the location of an integrated energy system for a typical new district is proposed. This method integrates the spatial location and energy load density of each functional district. Based on the timing characteristics and seasonal division of electric/thermal/cold load fluctuations, the improved fuzzy C means (FCM) algorithm combined with the density-based spatial clustering of applications with noise (DBSCAN) is applied to build different multi-scenarios in various functional districts, and a more representative ‘typical maximum load value’ is obtained. The architectural characteristics and the different energy utilization habits are combed on the weight of the electric/thermal/cold load for each functional district. By taking the minimum improved integrated load moment of the system and the minimum partition inner interval ratio as the objective functions, the location optimization model of an integrated energy supply centre in a typical new district is established, and the improved adaptive evolutionary immune algorithm is applied to the nested model. Finally, a typical new district in Northern China is taking as an example to verify the correctness and feasibility of the planning method proposed in this paper. |
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| AbstractList | In view of the obvious distinctions among energy utilization characteristics in various functional districts, a method for optimizing the location of an integrated energy system for a typical new district is proposed. This method integrates the spatial location and energy load density of each functional district. Based on the timing characteristics and seasonal division of electric/thermal/cold load fluctuations, the improved fuzzy C means (FCM) algorithm combined with the density-based spatial clustering of applications with noise (DBSCAN) is applied to build different multi-scenarios in various functional districts, and a more representative ‘typical maximum load value’ is obtained. The architectural characteristics and the different energy utilization habits are combed on the weight of the electric/thermal/cold load for each functional district. By taking the minimum improved integrated load moment of the system and the minimum partition inner interval ratio as the objective functions, the location optimization model of an integrated energy supply centre in a typical new district is established, and the improved adaptive evolutionary immune algorithm is applied to the nested model. Finally, a typical new district in Northern China is taking as an example to verify the correctness and feasibility of the planning method proposed in this paper. |
| Author | Duan, Jie Gao, Yajing Li, Hong Chen, Feifan Wang, Xiaodan |
| Author_xml | – sequence: 1 givenname: Hong surname: Li fullname: Li, Hong – sequence: 2 givenname: Xiaodan orcidid: 0000-0002-1059-2391 surname: Wang fullname: Wang, Xiaodan – sequence: 3 givenname: Jie surname: Duan fullname: Duan, Jie – sequence: 4 givenname: Feifan surname: Chen fullname: Chen, Feifan – sequence: 5 givenname: Yajing surname: Gao fullname: Gao, Yajing |
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| SubjectTerms | Energy improved adaptive evolutionary immune algorithm improved fuzzy C means algorithm integrated energy supply system integrated load density location optimization of energy supply centres |
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| Title | Locating Optimization of an Integrated Energy Supply Centre in a Typical New District Based on the Load Density |
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